A new bound-and-reduce approach of nonconvex quadratic programming problems

Yuelin Gao and Fei Wei

Applied Mathematics and Computation, 2015, vol. 250, issue C, 298-308

Abstract: For the nonconvex quadratic programming problem, a new linear programming relaxation bound-and-reduce algorithm is proposed and its convergence is proved. In this algorithm, a new hyper-rectangle partition technique and a new linear programming relaxation tactics are used. At the same time, the hyper-rectangular reduction method is used to raise its convergent speed. The numerical results demonstrate the effectiveness and feasibility of the proposed algorithm.

Keywords: Nonconvex quadratic programming; Global optimization; Branch-and-bound; Relaxation technique; Hyper-rectangle reducing (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:250:y:2015:i:c:p:298-308

DOI: 10.1016/j.amc.2014.10.077

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